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This study was performed to determine the identification (barcoding) using cytochrome oxidase gene of common carp, between three provinces of Golestan, Mazandaran and Gilan (respectively in Gomishan, Tajan and Kiashahr) in 2011. The results of sequencing showed that all samples from the three regions had a genetic distance less than 2%, so all samples were from the same species. The results of sequencing 30 tail samples of carp species on the southern shores of the Caspian Sea showed that all samples are of the same species and their genetic distance does not reach at least 2%. Therefore, all carp samples of the three provinces are of the same species and have the same type of barcode. In the study of nucleotide and haplotypic distance, Gomishan region was 10.75000, 1 and Kiashahr region were 3.200 and 0.9333, respectively. In the study of nucleotide diversity between the two regions, 0.01978 and the average nucleotide difference was 12.187. Haplotypic diversity in Gomishan region was 38.095 and in Kiashahr region was 23.809%. Out of 13 haplotypes, Gomishan region with 8 haplotypes (61.53%) and Kiashahr region with 5 haplotypes (38.46%) had the lowest haplotypes.The results of this study show that there is a significant difference between carp samples in Gomishan and Kiashahr regions in terms of nucleotide and haplotypic diversity (P <0.05).

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The purpose of this study was to evaluate the image processing technique in rice blast disease detection in field and controlled conditions. Using MATLAB software, images taken from field and controlled conditions were processed in three RGB, HSI and LAB color spaces. Then it was extracted by the gray area intensity profile, color properties, and threshold value for background image removal. After removing background in RGB, HSI and LAB color spaces, disease spots on rice leaf were determined. In RGB color space, by subtracting arrays by test and error, the blast patches on the leaf were separated from the rest of the image pixels. Hue was used in the HSI color space because this component was independent of light intensity variations, so blast blot identification was performed more accurately than the S and I components. In the LAB color space, the Kmeans clustering algorithm was used to segment the images into three clusters and was displayed in an independent cluster after labeling the image of blast disease spots. Finally, in order to determine the performance of the algorithms designed in three color spaces, the sensitivity factor, specificity and total accuracy were tested on the basis of the perturbation matrix for 500 image samples. In field and controlled conditions, the highest accuracy in detecting blast blots in the LAB color space was 94% and 98%, respectively. Overall, the results showed that the image processing method can be used to detect rice blast disease.